Mumtaz Ali
Environmental Engineering · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 1 researcher and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 32 citations into it; #4,475 of 5,479 by trust here, #4,337 by citations
Whom Mumtaz Ali trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ravinesh C. Deo
- C. Dawson
- Shan Suthaharan
- Jérôme Gilles
- C. T. ter Braak
- Vladimir Naumovich Vapnik
- A. Paulo
- Naveed Urr Rehman
- Leo Breiman
- Zhaohua Wu
Who trusts Mumtaz Ali overall
Papers from Mumtaz Ali's perspective
Ordered by how strongly the researchers Mumtaz Ali trusts endorse each paper. Same corpus, a different order for every researcher.
- The Nature of Statistical Learning Theory
- Statistical Learning Theory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Neural Networks for Pattern Recognition
- Gradient-based learning applied to document recognition
- Support-Vector Networks
- Regression Shrinkage and Selection via the Lasso
- Bagging Predictors
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Their papers
- Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts (2020) — 175 citations
- Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms (2019) — 90 citations
- Cotton yield prediction with Markov Chain Monte Carlo-based simulation model integrated with genetic programing algorithm: A new hybrid copula-driven approach (2018) — 50 citations
- An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index (2018) — 75 citations